Noise and Vibration Analysis

Brandt, Anders

doi:10.1002/9780470978160

Cited by 370 publications

(132 citation statements)

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“…It is especially important to understand the lowest degree of terrain roughness that the system can appreciate. In general, it is known that the relationship between root-mean-square (rms) and PSD of an elevation profile can be expressed as [32] = ф(Ω)…”

Section: System Sensitivitymentioning

confidence: 99%

Mind the ground: A power spectral density-based estimator for all-terrain rovers

et al. 2020

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There is a growing interest in new sensing technologies and processing algorithms to increase the level of driving automation towards self-driving vehicles. The challenge for autonomy is especially difficult for the negotiation of uncharted scenarios, including natural terrain. This paper proposes a method for terrain unevenness estimation that is based on the power spectral density (PSD) of the surface profile as measured by exteroceptive sensing, that is, by using a common onboard range sensor such as a stereoscopic camera. Using these components, the proposed estimator can evaluate terrain on-line during normal operations. PSD-based analysis provides insight not only on the magnitude of irregularities, but also on how these irregularities are distributed at various wavelengths. A feature vector can be defined to classify roughness that is proved a powerful statistical tool for the characterization of a given terrain fingerprint showing a limited sensitivity to vehicle tilt rotations. First, the theoretical foundations behind the PSD-based estimator are presented. Then, the system is validated in the field using an all-terrain rover that operates on various natural surfaces. It is shown its potential for automatic ground harshness estimation and, in general, for the development of driving assistance systems.

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Section: System Sensitivitymentioning

confidence: 99%

Mind the ground: A power spectral density-based estimator for all-terrain rovers

et al. 2020

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“…From a signal-processing point of view, a general array corresponds to a spatial window function that is used to sample the wave field [33]. The sampling in the radial offset (space) domain represents a multiplication of the spatial array window function and the physical wave field.…”

Section: Variation Of Phase Velocity: Polar Angle and 2d Imagingmentioning

confidence: 99%

Lamb Wave Phase Velocity Imaging of Concrete Plates with 2D Arrays

Tofeldt

Rydén

2017

J Nondestruct Eval

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In the nondestructive evaluation of concrete structures, ultrasonic techniques are considered to be more capable than lowfrequency techniques such as the impact-echo method. This is especially true with the recent development of ultrasonic transducers, synthetic apertures, and results in an image form, and because low-frequency techniques are usually limited in their evaluation to the frequency of one single resonant mode. With the aim of reducing this gap in capabilities, we present a 2D array and wide-frequency bandwidth technique for Lamb wave phase velocity imaging. The presentation involves a measurement on a newly cast concrete plate using a hammer and an accelerometer as an example. The key concept of the technique is the use of 2D arrays that record a full wave field response over a limited surface subdomain within the complete measurement domain. Through a discrete Fourier transform, a spectral estimate is obtained for the 2D array in the frequencyphase velocity domain. The variation of the phase velocity is then mapped using a stepwise movement of the 2D array within the complete measurement domain. With two different types of 2D arrays, the variation of the phase velocity for the A0 Lamb mode is mapped and displayed in a polar and image plot, and low variation is observed for both cases. This result verifies the expected condition of a homogenous material and plate thickness and, more importantly, highlights the potential of wide-frequency bandwidth techniques based on full wave field data.

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“…Period of signal (with ADC simple) was determined according to l > 0 by which autocorrelation coefficient is maximal. If minimal autocorrelation coefficient fits half-period shift, then period was determined correctly [4]. Period of signal make up 12.5 microseconds and carrier frequency make up 80 kHz for diagram in figure 3.…”

Section: Adc Samplementioning

confidence: 99%

Usage Autocorrelation Function in the Capacity of Indicator Shape of the Signal in Acoustic Emission Testing of Intricate Castings

Popkov

2016

J. Phys.: Conf. Ser.

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Abstract. The article contains information about acoustic emission signals analysing using autocorrelation function. Operation factors were analysed, such as shape of signal, the origins time and carrier frequency. The purpose of work is estimating the validity of correlations methods analysing signals. Acoustic emission signal consist of different types of waves, which propagate on different trajectories in object of control. Acoustic emission signal is amplitude-, phase-and frequency-modeling signal. It was described by carrier frequency at a given point of time. Period of signal make up 12.5 microseconds and carrier frequency make up 80 kHz for analysing signal. Usage autocorrelation function like indicator the origin time of acoustic emission signal raises validity localization of emitters.

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Noise and Vibration Analysis

Cited by 370 publications

References 0 publications

Mind the ground: A power spectral density-based estimator for all-terrain rovers

Mind the ground: A power spectral density-based estimator for all-terrain rovers

Lamb Wave Phase Velocity Imaging of Concrete Plates with 2D Arrays

Usage Autocorrelation Function in the Capacity of Indicator Shape of the Signal in Acoustic Emission Testing of Intricate Castings

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